Large-scale global optimization (LSGO) problems have widely appeared in various real-
world applications. However, their inherent complexity, coupled with the curse of …

The generation and updating of solutions, eg, crossover and mutation, of many existing
evolutionary algorithms directly operate on decision variables. The operators are very time …

There are usually multiple constraints in constrained multiobjective optimization. Those
constraints reduce the feasible area of the constrained multiobjective optimization problems …

With the popularity of “flipped classrooms,” teachers pay more attention to cultivating
students' autonomous learning ability while imparting knowledge. Inspired by this, this paper …

Sparse large scale multiobjective optimization problems (sparse LSMOPs) contain
numerous decision variables, and their Pareto optimal solutions' decision variables are very …

With growing attention focused on energy generation and environmental conservation, the
operation of cascade reservoirs that considers power generation benefits and ecological …

As an effective approximation algorithm for multi-objective jobshop scheduling, multi-
objective evolutionary algorithms (MOEAs) have received extensive attention. However …

In large-scale multiobjective optimization, the huge search space poses a great challenge to
the convergence search of existing evolutionary algorithms. A fuzzy decision variables …

In the realm of high-dimensional problem spaces, particle swarm optimizers have been
found to exhibit unnecessary roaming behavior. In response, this paper proposes a …

As large-scale multiobjective optimization problems (LSMOPs) contain many decision
variables, most existing large-scale multiobjective optimization algorithms (LSMOEAs) …